tfm.nlp.encoders.ReuseEncoderConfig

Reuse encoder configuration.

Inherits From: Config, ParamsDict

BUILDER

default_params Dataclass field
restrictions Dataclass field
vocab_size Dataclass field
hidden_size Dataclass field
num_layers Dataclass field
num_attention_heads Dataclass field
hidden_activation Dataclass field
intermediate_size Dataclass field
dropout_rate Dataclass field
attention_dropout_rate Dataclass field
max_position_embeddings Dataclass field
type_vocab_size Dataclass field
initializer_range Dataclass field
embedding_size Dataclass field
output_range Dataclass field
return_all_encoder_outputs Dataclass field
norm_first Dataclass field
reuse_attention Dataclass field
use_relative_pe Dataclass field
pe_max_seq_length Dataclass field
max_reuse_layer_idx Dataclass field

Methods

as_dict

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Returns a dict representation of params_dict.ParamsDict.

For the nested params_dict.ParamsDict, a nested dict will be returned.

from_args

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Builds a config from the given list of arguments.

from_json

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Wrapper for from_yaml.

from_yaml

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get

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Accesses through built-in dictionary get method.

lock

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Makes the ParamsDict immutable.

override

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Override the ParamsDict with a set of given params.

Args
override_params a dict or a ParamsDict specifying the parameters to be overridden.
is_strict a boolean specifying whether override is strict or not. If True, keys in override_params must be present in the ParamsDict. If False, keys in override_params can be different from what is currently defined in the ParamsDict. In this case, the ParamsDict will be extended to include the new keys.

replace

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Overrides/returns a unlocked copy with the current config unchanged.

validate

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Validate the parameters consistency based on the restrictions.

This method validates the internal consistency using the pre-defined list of restrictions. A restriction is defined as a string which specfiies a binary operation. The supported binary operations are {'==', '!=', '<', '<=', '>', '>='}. Note that the meaning of these operators are consistent with the underlying Python immplementation. Users should make sure the define restrictions on their type make sense.

For example, for a ParamsDict like the following

a:
  a1: 1
  a2: 2
b:
  bb:
    bb1: 10
    bb2: 20
  ccc:
    a1: 1
    a3: 3

one can define two restrictions like this ['a.a1 == b.ccc.a1', 'a.a2 <= b.bb.bb2']

What it enforces are

  • a.a1 = 1 == b.ccc.a1 = 1
  • a.a2 = 2 <= b.bb.bb2 = 20

Raises
KeyError if any of the following happens (1) any of parameters in any of restrictions is not defined in ParamsDict, (2) any inconsistency violating the restriction is found.
ValueError if the restriction defined in the string is not supported.

__contains__

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Implements the membership test operator.

__eq__

IMMUTABLE_TYPES (<class 'str'>, <class 'int'>, <class 'float'>, <class 'bool'>, <class 'NoneType'>)
RESERVED_ATTR ['_locked', '_restrictions']
SEQUENCE_TYPES (<class 'list'>, <class 'tuple'>)
attention_dropout_rate 0.1
default_params None
dropout_rate 0.1
embedding_size None
hidden_activation 'gelu'
hidden_size 768
initializer_range 0.02
intermediate_size 3072
max_position_embeddings 512
max_reuse_layer_idx 6
norm_first False
num_attention_heads 12
num_layers 12
output_range None
pe_max_seq_length 512
restrictions None
return_all_encoder_outputs False
reuse_attention -1
type_vocab_size 2
use_relative_pe False
vocab_size 30522